[SPARK-28969][PYTHON][ML] OneVsRestParams parity between scala and py…

…thon

### What changes were proposed in this pull request?
Follow the scala ```OneVsRestParams``` implementation, move ```setClassifier``` from ```OneVsRestParams``` to ```OneVsRest``` in Pyspark

### Why are the changes needed?
1. Maintain the parity between scala and python code.
2. ```Classifier``` can only be set in the estimator.

### Does this PR introduce any user-facing change?
Yes.
Previous behavior: ```OneVsRestModel``` has method ```setClassifier```
Current behavior:  ```setClassifier``` is removed from ```OneVsRestModel```. ```classifier``` can only be set in ```OneVsRest```.

### How was this patch tested?
Use existing tests

Closes #25715 from huaxingao/spark-28969.

Authored-by: Huaxin Gao <[email protected]>
Signed-off-by: Sean Owen <[email protected]>

python/pyspark/ml/classification.py

@@ -1872,15 +1872,6 @@ class OneVsRestParams(HasFeaturesCol, HasLabelCol, HasWeightCol, HasPredictionCo
 
     classifier = Param(Params._dummy(), "classifier", "base binary classifier")
 
-    @since("2.0.0")
-    def setClassifier(self, value):
-        """
-        Sets the value of :py:attr:`classifier`.
-
-        .. note:: Only LogisticRegression and NaiveBayes are supported now.
-        """
-        return self._set(classifier=value)
-
     @since("2.0.0")
     def getClassifier(self):
         """
@@ -1959,6 +1950,13 @@ def setParams(self, featuresCol="features", labelCol="label", predictionCol="pre
         kwargs = self._input_kwargs
         return self._set(**kwargs)
 
+    @since("2.0.0")
+    def setClassifier(self, value):
+        """
+        Sets the value of :py:attr:`classifier`.
+        """
+        return self._set(classifier=value)
+
     def _fit(self, dataset):
         labelCol = self.getLabelCol()
         featuresCol = self.getFeaturesCol()
@@ -2212,7 +2210,8 @@ def _from_java(cls, java_stage):
         classifier = JavaParams._from_java(java_stage.getClassifier())
         models = [JavaParams._from_java(model) for model in java_stage.models()]
         py_stage = cls(models=models).setPredictionCol(predictionCol).setLabelCol(labelCol)\
-            .setFeaturesCol(featuresCol).setClassifier(classifier)
+            .setFeaturesCol(featuresCol)
+        py_stage._set(classifier=classifier)
         py_stage._resetUid(java_stage.uid())
         return py_stage