Github user petro-rudenko commented on the pull request:
https://github.com/apache/spark/pull/5510#issuecomment-93412249
Ideally crossvalidator should handle next cases:
1) No parameters at all: just run est.fit(dataset, new ParamMap)
2) 1 param: set this param to estimator (assume it's a weird way to
override default param) and again do step 1.
3) 2+ params: do crossvalidation.
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