Github user jkbradley commented on the issue:
https://github.com/apache/spark/pull/19904
Strong +1 for unpersisting the data at the end. In the long-term, I don't
think we'll even cache the training and validation datasets. Our caching of
the training & validation datasets is a temporary hack to get around the issue
that we don't have a DataFrame k-fold splitting method. Our current workaround
is to go down to RDDs: DataFrame -> RDD -> k-fold split -> DataFrame, and as I
recall, we cache to lower the SerDe costs in these conversions. Once we have a
k-fold split method for DataFrames, we can just cache the original (full)
dataset and not the k splits.
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