The correct option is Split Data module.

The Split Data module is designed to partition a dataset into two subsets and it lets you specify the fraction or the number of rows to allocate to each subset. You can choose random sampling and set a random seed for reproducibility and you can also use stratified sampling when you need to preserve class distributions between the training and holdout sets.

The Group Data into Bins module is intended to convert continuous numeric variables into discrete bins for feature engineering. It does not split the dataset into separate training and holdout sets.

The Clip Values module is used to cap or floor numeric values to handle outliers or enforce limits. It modifies values rather than partitioning the data so it will not create training and holdout subsets.

The Group Categorical Values module is used to combine or regroup categories in a categorical feature to reduce cardinality. It is a transformation tool and it does not perform dataset splitting.

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