Combining H2O cross validation with parameter optimization

Hello!

I am undertating a study which seeks to predict whether a granite instrusion is prospective for ore deposits on the basis of geochemical data. For this study, I am comparing random forest and Gradient boosting. What I aim to do is to combine cross-validation and parameter optimization together in the context of the H2O random forest and gradient boost nodes. Q: Is there a way to succesfully combine the H2O cross-validation loop with the parameter optimization loop without causing loop issues? The study, that I am attempting to replicate says: “Model tuning was accomplished using a grid search strategy with the fivefold cross-validation method.” For reference this is the current KNIME set-up.

Many thanks in advance for any assistance!

~Cole K.