[SPARK-14498][ML][PYTHON][SQL] Many cleanups to ML and ML-related docs

## What changes were proposed in this pull request?

Cleanups to documentation.  No changes to code.
* GBT docs: Move Scala doc for private object GradientBoostedTrees to public docs for GBTClassifier,Regressor
* GLM regParam: needs doc saying it is for L2 only
* TrainValidationSplitModel: add .. versionadded:: 2.0.0
* Rename “_transformer_params_from_java” to “_transfer_params_from_java”
* LogReg Summary classes: “probability” col should not say “calibrated”
* LR summaries: coefficientStandardErrors —> document that intercept stderr comes last.  Same for t,p-values
* approxCountDistinct: Document meaning of “rsd" argument.
* LDA: note which params are for online LDA only

## How was this patch tested?

Doc build

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #12266 from jkbradley/ml-doc-cleanups.

4 files changed, 13 insertions, 2 deletions

diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index d98919b3c6..e64c7a392b 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -291,7 +291,7 @@ class LogisticRegressionSummary(JavaCallable):
     @since("2.0.0")
     def probabilityCol(self):
         """
-        Field in "predictions" which gives the calibrated probability
+        Field in "predictions" which gives the probability
         of each class as a vector.
         """
         return self._call_java("probabilityCol")
diff --git a/python/pyspark/ml/regression.py b/python/pyspark/ml/regression.py
index f6c5d130dd..1c18df3b27 100644
--- a/python/pyspark/ml/regression.py
+++ b/python/pyspark/ml/regression.py
@@ -331,6 +331,9 @@ class LinearRegressionSummary(JavaCallable):
         Standard error of estimated coefficients and intercept.
         This value is only available when using the "normal" solver.
 
+        If :py:attr:`LinearRegression.fitIntercept` is set to True,
+        then the last element returned corresponds to the intercept.
+
         .. seealso:: :py:attr:`LinearRegression.solver`
         """
         return self._call_java("coefficientStandardErrors")
@@ -342,6 +345,9 @@ class LinearRegressionSummary(JavaCallable):
         T-statistic of estimated coefficients and intercept.
         This value is only available when using the "normal" solver.
 
+        If :py:attr:`LinearRegression.fitIntercept` is set to True,
+        then the last element returned corresponds to the intercept.
+
         .. seealso:: :py:attr:`LinearRegression.solver`
         """
         return self._call_java("tValues")
@@ -353,6 +359,9 @@ class LinearRegressionSummary(JavaCallable):
         Two-sided p-value of estimated coefficients and intercept.
         This value is only available when using the "normal" solver.
 
+        If :py:attr:`LinearRegression.fitIntercept` is set to True,
+        then the last element returned corresponds to the intercept.
+
         .. seealso:: :py:attr:`LinearRegression.solver`
         """
         return self._call_java("pValues")
diff --git a/python/pyspark/ml/tuning.py b/python/pyspark/ml/tuning.py
index da00f317b3..ea8c61b7ef 100644
--- a/python/pyspark/ml/tuning.py
+++ b/python/pyspark/ml/tuning.py
@@ -588,6 +588,8 @@ class TrainValidationSplit(Estimator, ValidatorParams, MLReadable, MLWritable):
 class TrainValidationSplitModel(Model, ValidatorParams, MLReadable, MLWritable):
     """
     Model from train validation split.
+
+    .. versionadded:: 2.0.0
     """
 
     def __init__(self, bestModel):
diff --git a/python/pyspark/ml/wrapper.py b/python/pyspark/ml/wrapper.py
index a2cf2296fb..bbeb6cfe6f 100644
--- a/python/pyspark/ml/wrapper.py
+++ b/python/pyspark/ml/wrapper.py
@@ -249,7 +249,7 @@ class JavaModel(Model, JavaCallable, JavaTransformer):
         """
         Initialize this instance with a Java model object.
         Subclasses should call this constructor, initialize params,
-        and then call _transformer_params_from_java.
+        and then call _transfer_params_from_java.
 
         This instance can be instantiated without specifying java_model,
         it will be assigned after that, but this scenario only used by