[SPARK-15195][PYSPARK][DOCS] Update ml.tuning PyDocs

## What changes were proposed in this pull request?

Tag classes in ml.tuning as experimental, add docs for kfolds avg metric, and copy TrainValidationSplit scaladoc for more detailed explanation.

## How was this patch tested?

built docs locally

Author: Holden Karau <holden@us.ibm.com>

Closes #12967 from holdenk/SPARK-15195-pydoc-ml-tuning.

1 files changed, 15 insertions, 1 deletions

diff --git a/python/pyspark/ml/tuning.py b/python/pyspark/ml/tuning.py
index b21cf92559..0920ae6ea1 100644
--- a/python/pyspark/ml/tuning.py
+++ b/python/pyspark/ml/tuning.py
@@ -33,6 +33,8 @@ __all__ = ['ParamGridBuilder', 'CrossValidator', 'CrossValidatorModel', 'TrainVa
 
 class ParamGridBuilder(object):
     r"""
+    .. note:: Experimental
+
     Builder for a param grid used in grid search-based model selection.
 
     >>> from pyspark.ml.classification import LogisticRegression
@@ -143,6 +145,8 @@ class ValidatorParams(HasSeed):
 
 class CrossValidator(Estimator, ValidatorParams):
     """
+    .. note:: Experimental
+
     K-fold cross validation.
 
     >>> from pyspark.ml.classification import LogisticRegression
@@ -260,6 +264,8 @@ class CrossValidator(Estimator, ValidatorParams):
 
 class CrossValidatorModel(Model, ValidatorParams):
     """
+    .. note:: Experimental
+
     Model from k-fold cross validation.
 
     .. versionadded:: 1.4.0
@@ -269,6 +275,8 @@ class CrossValidatorModel(Model, ValidatorParams):
         super(CrossValidatorModel, self).__init__()
         #: best model from cross validation
         self.bestModel = bestModel
+        #: Average cross-validation metrics for each paramMap in
+        #: CrossValidator.estimatorParamMaps, in the corresponding order.
         self.avgMetrics = avgMetrics
 
     def _transform(self, dataset):
@@ -294,7 +302,11 @@ class CrossValidatorModel(Model, ValidatorParams):
 
 class TrainValidationSplit(Estimator, ValidatorParams):
     """
-    Train-Validation-Split.
+    .. note:: Experimental
+
+    Validation for hyper-parameter tuning. Randomly splits the input dataset into train and
+    validation sets, and uses evaluation metric on the validation set to select the best model.
+    Similar to :class:`CrossValidator`, but only splits the set once.
 
     >>> from pyspark.ml.classification import LogisticRegression
     >>> from pyspark.ml.evaluation import BinaryClassificationEvaluator
@@ -405,6 +417,8 @@ class TrainValidationSplit(Estimator, ValidatorParams):
 
 class TrainValidationSplitModel(Model, ValidatorParams):
     """
+    .. note:: Experimental
+
     Model from train validation split.
 
     .. versionadded:: 2.0.0