Use a fixed 5-fold cross-validation with no adjustments; interpret results based on average performance.

Set up a customizable cross-validation process using Spark ML's CrossValidator; select the number of folds based on dataset size and variability, and interpret results by analyzing performance metrics across folds to identify optimal hyperparameters.

Perform cross-validation once with a single fold; interpret results directly without optimization.

Outsource cross-validation to a third-party service; interpret results based on service recommendations.