Kfold cross validation randomly divides the data into k subsets. In turn, each of the k sets is used as a validation set while the remaining data are used as a training set to fit the model. In total, k models are fit and k validation statistics are obtained. The model giving the best validation statistic is chosen as the final model. This method is useful for small data sets, because it makes efficient use of limited amounts of data.
In JMP, select KFold Crossvalidation from the red triangle options for Stepwise Fit.
In JMP Pro, you can access kfold cross validation in two ways:
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From the red triangle options for Stepwise Fit, select KFold Crossvalidation.

When you use kfold cross validation, the Stopping Rule defaults to Max KFold RSquare. This rule attempts to maximize the RSquare KFold statistic.
Note: Max KFold RSquare considers only the models defined by pvalue entry (Forward direction) or removal (Backward direction). It does not consider all possible models.
The Max KFold RSquare stopping rule behaves in a fashion similar to the Max Validation RSquare stopping rule. See Max Validation RSquare. Replace references to RSquare Validation with RSquare KFold.