diff --git a/examples/sklearn-hpo-cv.py b/examples/sklearn-hpo-cv.py
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+"""Random Search with CVEvaluation.
+
+This example demonstrates the [`CVEvaluation`][amltk.sklearn.CVEvaluation] class,
+which builds a custom cross-validation task that can be used to evaluate
+[`pipelines`](../guides/pipelines.md) with cross-validation, using
+[`RandomSearch`][amltk.optimization.optimizers.random_search.RandomSearch].
+"""
+from collections.abc import Mapping
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import openml
+import pandas as pd
+from ConfigSpace import Categorical, Integer
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.impute import SimpleImputer
+from sklearn.metrics import get_scorer
+from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder
+
+from amltk.optimization.optimizers.random_search import RandomSearch
+from amltk.optimization.trial import Metric, Trial
+from amltk.pipeline import Choice, Component, Node, Sequential, Split, request
+from amltk.sklearn import CVEvaluation
+
+
+def get_fold(
+    openml_task_id: int,
+    fold: int,
+) -> tuple[
+    pd.DataFrame,
+    pd.DataFrame,
+    pd.DataFrame | pd.Series,
+    pd.DataFrame | pd.Series,
+]:
+    """Get the data for a specific fold of an OpenML task.
+
+    Args:
+        openml_task_id: The OpenML task id.
+        fold: The fold number.
+        n_splits: The number of splits that will be applied. This is used
+            to resample training data such that enough at least instance for each class is present for
+            every stratified split.
+        seed: The random seed to use for reproducibility of resampling if necessary.
+    """
+    task = openml.tasks.get_task(
+        openml_task_id,
+        download_splits=True,
+        download_data=True,
+        download_qualities=True,
+        download_features_meta_data=True,
+    )
+    train_idx, test_idx = task.get_train_test_split_indices(fold=fold)
+    X, y = task.get_X_and_y(dataset_format="dataframe")  # type: ignore
+    X_train, y_train = X.iloc[train_idx], y.iloc[train_idx]
+    X_test, y_test = X.iloc[test_idx], y.iloc[test_idx]
+    return X_train, X_test, y_train, y_test
+
+
+preprocessing = Split(
+    {
+        "numerical": Component(SimpleImputer, space={"strategy": ["mean", "median"]}),
+        "categorical": [
+            Component(
+                OrdinalEncoder,
+                config={
+                    "categories": "auto",
+                    "handle_unknown": "use_encoded_value",
+                    "unknown_value": -1,
+                    "encoded_missing_value": -2,
+                },
+            ),
+            Choice(
+                "passthrough",
+                Component(
+                    OneHotEncoder,
+                    space={"max_categories": (2, 20)},
+                    config={
+                        "categories": "auto",
+                        "drop": None,
+                        "sparse_output": False,
+                        "handle_unknown": "infrequent_if_exist",
+                    },
+                ),
+                name="one_hot",
+            ),
+        ],
+    },
+    name="preprocessing",
+)
+
+
+def rf_config_transform(config: Mapping[str, Any], _: Any) -> dict[str, Any]:
+    new_config = dict(config)
+    if new_config["class_weight"] == "None":
+        new_config["class_weight"] = None
+    return new_config
+
+
+# NOTE: This space should not be used for evaluating how good this RF is
+# vs other algorithms
+rf_classifier = Component(
+    item=RandomForestClassifier,
+    config_transform=rf_config_transform,
+    space={
+        "criterion": ["gini", "entropy"],
+        "max_features": Categorical(
+            "max_features",
+            list(np.logspace(0.1, 1, base=10, num=10) / 10),
+            ordered=True,
+        ),
+        "min_samples_split": Integer("min_samples_split", bounds=(2, 20), default=2),
+        "min_samples_leaf": Integer("min_samples_leaf", bounds=(1, 20), default=1),
+        "bootstrap": Categorical("bootstrap", [True, False], default=True),
+        "class_weight": ["balanced", "balanced_subsample", "None"],
+        "min_impurity_decrease": (1e-9, 1e-1),
+    },
+    config={
+        "random_state": request(
+            "random_state",
+            default=None,
+        ),  # Will be provided later by the `Trial`
+        "n_estimators": 512,
+        "max_depth": None,
+        "min_weight_fraction_leaf": 0.0,
+        "max_leaf_nodes": None,
+        "warm_start": False,  # False due to no iterative fit used here
+        "n_jobs": 1,
+    },
+)
+
+rf_pipeline = Sequential(preprocessing, rf_classifier, name="rf_pipeline")
+
+
+def do_something_after_a_split_was_evaluated(
+    trial: Trial,
+    fold: int,
+    info: CVEvaluation.PostSplitInfo,
+) -> CVEvaluation.PostSplitInfo:
+    return info
+
+
+def do_something_after_a_complete_trial_was_evaluated(
+    report: Trial.Report,
+    pipeline: Node,
+    info: CVEvaluation.CompleteEvalInfo,
+) -> Trial.Report:
+    return report
+
+
+def main() -> None:
+    random_seed = 42
+    openml_task_id = 31  # Adult dataset, classification
+    task_hint = "classification"
+    outer_fold_number = (
+        0  # Only run the first outer fold, wrap this in a loop if needs be, with a unique history file
+        # for each one
+    )
+    optimizer_cls = RandomSearch
+    working_dir = Path("example-sklearn-hpo-cv").absolute()
+    results_to = working_dir / "results.parquet"
+    inner_fold_seed = random_seed + outer_fold_number
+    metric_definition = Metric(
+        "accuracy",
+        minimize=False,
+        bounds=(0, 1),
+        fn=get_scorer("accuracy"),
+    )
+
+    per_process_memory_limit = None  # (4, "GB")  # NOTE: May have issues on Mac
+    per_process_walltime_limit = None  # (60, "s")
+
+    debugging = False
+    if debugging:
+        max_trials = 1
+        max_time = 30
+        n_workers = 1
+        # raise an error with traceback, something went wrong
+        on_trial_exception = "raise"
+        display = True
+        wait_for_all_workers_to_finish = True
+    else:
+        max_trials = 10
+        max_time = 300
+        n_workers = 4
+        # Just mark the trial as fail and move on to the next one
+        on_trial_exception = "continue"
+        display = True
+        wait_for_all_workers_to_finish = False
+
+    X, X_test, y, y_test = get_fold(
+        openml_task_id=openml_task_id,
+        fold=outer_fold_number,
+    )
+
+    # This object below is a highly customizable class to create a function that we can use for
+    # evaluating pipelines.
+    evaluator = CVEvaluation(
+        # Provide data, number of times to split, cross-validation and a hint of the task type
+        X,
+        y,
+        splitter="cv",
+        n_splits=8,
+        task_hint=task_hint,
+        # Seeding for reproducibility
+        random_state=inner_fold_seed,
+        # Provide test data to get test scores
+        X_test=X_test,
+        y_test=y_test,
+        # Record training scores
+        train_score=True,
+        # Where to store things
+        working_dir=working_dir,
+        # What to do when something goes wrong.
+        on_error="raise" if on_trial_exception == "raise" else "fail",
+        # Whether you want models to be store on disk under working_dir
+        store_models=False,
+        # A callback to be called at the end of each split
+        post_split=do_something_after_a_split_was_evaluated,
+        # Some callback that is called at the end of all fold evaluations
+        post_processing=do_something_after_a_complete_trial_was_evaluated,
+        # Whether the post_processing callback requires models will required models, i.e.
+        # to compute some bagged average over all fold models. If `False` will discard models eagerly
+        # to sasve sapce.
+        post_processing_requires_models=False,
+        # This handles edge cases related to stratified splitting when there are too
+        # few instances of a specific class. May wish to disable if your passing extra fit params
+        rebalance_if_required_for_stratified_splitting=True,
+        # Extra parameters requested by sklearn models/group splitters or metrics,
+        # such as `sample_weight`
+        params=None,
+    )
+
+    # Here we just use the `optimize` method to setup and run an optimization loop
+    # with `n_workers`. Please either look at the source code for `optimize` or
+    # refer to the `Scheduler` and `Optimizer` guide if you need more fine grained control.
+    # If you need to evaluate a certain configuraiton, you can create your own `Trial` object.
+    #
+    # trial = Trial.create(name=...., info=None, config=..., bucket=..., seed=..., metrics=metric_def)
+    # report = evaluator.evaluate(trial, rf_pipeline)
+    # print(report)
+    #
+    history = rf_pipeline.optimize(
+        target=evaluator.fn,
+        metric=metric_definition,
+        optimizer=optimizer_cls,
+        seed=inner_fold_seed,
+        process_memory_limit=per_process_memory_limit,
+        process_walltime_limit=per_process_walltime_limit,
+        working_dir=working_dir,
+        max_trials=max_trials,
+        timeout=max_time,
+        display=display,
+        wait=wait_for_all_workers_to_finish,
+        n_workers=n_workers,
+        on_trial_exception=on_trial_exception,
+    )
+
+    df = history.df()
+
+    # Assign some new information to the dataframe
+    df.assign(
+        outer_fold=outer_fold_number,
+        inner_fold_seed=inner_fold_seed,
+        task_id=openml_task_id,
+        max_trials=max_trials,
+        max_time=max_time,
+        optimizer=optimizer_cls.__name__,
+        n_workers=n_workers,
+    )
+    print(df)
+    print(f"Saving dataframe of results to path: {results_to}")
+    df.to_parquet(results_to)
+
+
+if __name__ == "__main__":
+    main()