[Data] Remove 'Using Preprocessors' (#44006) (#44042)

Cherry-pick of #44006.
We typically don't recommend people use Preprocessor anymore. So, this PR removes the "Using Preprocessors" user guide.

Signed-off-by: Balaji Veeramani <balaji@anyscale.com>

Author: bveeramani

doc/source/data/preprocessors.rst

@@ -23,6 +23,5 @@ show you how achieve several tasks.
     working-with-pytorch
     batch_inference
     performance-tips
-    preprocessors
     monitoring-your-workload
     custom-datasource-example

doc/source/data/user-guide.rst

@@ -204,7 +204,7 @@ machines have 16 CPUs in addition to the 4 GPUs, each actor should have
 How to preprocess data for training?
 ------------------------------------
 
-Particularly for tabular data, Ray Data comes with out-of-the-box :ref:`preprocessors <data-preprocessors>` that implement common feature preprocessing operations.
+Particularly for tabular data, Ray Data comes with out-of-the-box :ref:`preprocessors <preprocessor-ref>` that implement common feature preprocessing operations.
 You can use this with Ray Train Trainers by applying them on the dataset before passing the dataset into a Trainer. For example:
 
 

doc/source/train/distributed-xgboost-lightgbm.rst

@@ -510,7 +510,7 @@ Preprocessing structured data
     This section is for tabular/structured data. The recommended way for preprocessing unstructured data is to use
     Ray Data operations such as `map_batches`. See the :ref:`Ray Data Working with Pytorch guide <working_with_pytorch>` for more details.
 
-For tabular data, we recommend using Ray Data :ref:`preprocessors <data-preprocessors>`, which implement common data preprocessing operations.
+For tabular data, we recommend using Ray Data :ref:`preprocessors <preprocessor-ref>`, which implement common data preprocessing operations.
 You can use this with Ray Train Trainers by applying them on the dataset before passing the dataset into a Trainer. For example:
 
 .. testcode::