Calling XGBModel.fit() should clear the Booster by default (#6562)

* Calling XGBModel.fit() should clear the Booster by default

* Document the behavior of fit()

* Allow sklearn object to be passed in directly via xgb_model argument

* Fix lint

python-package/xgboost/sklearn.py

@@ -501,8 +501,10 @@ def _configure_fit(
         eval_metric: Optional[Union[Callable, str, List[str]]],
         params: Dict[str, Any],
     ) -> Tuple[Booster, Optional[Union[Callable, str, List[str]]], Dict[str, Any]]:
-        model = self._Booster if hasattr(self, "_Booster") else None
-        model = booster if booster is not None else model
+        # pylint: disable=protected-access, no-self-use
+        model = booster
+        if hasattr(model, '_Booster'):
+            model = model._Booster  # Handle the case when xgb_model is a sklearn model object
         feval = eval_metric if callable(eval_metric) else None
         if eval_metric is not None:
             if callable(eval_metric):
@@ -518,7 +520,11 @@ def fit(self, X, y, *, sample_weight=None, base_margin=None,
             feature_weights=None,
             callbacks=None):
         # pylint: disable=invalid-name,attribute-defined-outside-init
-        """Fit gradient boosting model
+        """Fit gradient boosting model.
+
+        Note that calling ``fit()`` multiple times will cause the model object to be re-fit from
+        scratch. To resume training from a previous checkpoint, explicitly pass ``xgb_model``
+        argument.
 
         Parameters
         ----------
@@ -1212,6 +1218,10 @@ def fit(self, X, y, *, group, sample_weight=None, base_margin=None,
         # pylint: disable = attribute-defined-outside-init,arguments-differ
         """Fit gradient boosting ranker
 
+        Note that calling ``fit()`` multiple times will cause the model object to be re-fit from
+        scratch. To resume training from a previous checkpoint, explicitly pass ``xgb_model``
+        argument.
+
         Parameters
         ----------
         X : array_like
@@ -1322,6 +1332,9 @@ def fit(self, X, y, *, group, sample_weight=None, base_margin=None,
                 raise ValueError(
                     'Custom evaluation metric is not yet supported for XGBRanker.')
             params.update({'eval_metric': eval_metric})
+        if hasattr(xgb_model, '_Booster'):
+            # Handle the case when xgb_model is a sklearn model object
+            xgb_model = xgb_model._Booster  # pylint: disable=protected-access
 
         self._Booster = train(params, train_dmatrix,
                               self.n_estimators,