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[SPARK-21911][ML][FOLLOW-UP] Fix doc for parallel ML Tuning in PySpark #19641

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[SPARK-21911][ML][FOLLOW-UP] Fix doc for parallel ML Tuning in PySpark #19641

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2 changes: 1 addition & 1 deletion docs/ml-tuning.md
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Expand Up @@ -55,7 +55,7 @@ for multiclass problems. The default metric used to choose the best `ParamMap` c
method in each of these evaluators.

To help construct the parameter grid, users can use the [`ParamGridBuilder`](api/scala/index.html#org.apache.spark.ml.tuning.ParamGridBuilder) utility.
By default, sets of parameters from the parameter grid are evaluated in serial. Parameter evaluation can be done in parallel by setting `parallelism` with a value of 2 or more (a value of 1 will be serial) before running model selection with `CrossValidator` or `TrainValidationSplit` (NOTE: this is not yet supported in Python).
By default, sets of parameters from the parameter grid are evaluated in serial. Parameter evaluation can be done in parallel by setting `parallelism` with a value of 2 or more (a value of 1 will be serial) before running model selection with `CrossValidator` or `TrainValidationSplit`.
The value of `parallelism` should be chosen carefully to maximize parallelism without exceeding cluster resources, and larger values may not always lead to improved performance. Generally speaking, a value up to 10 should be sufficient for most clusters.

# Cross-Validation
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