AutoML Hyperparameter Optimization provides best practices for designing sound hyperparameter searches and utilizing tooling like Optuna, Ray Tune, and PyCaret without bypassing problem framing, validation design, or explainability. It covers search-space design, validation schemes, fitting preprocessing inside folds, and reporting against baselines. Reach for it when tuning model hyperparameters, setting up a pruned or distributed search, designing a nested validation scheme, or evaluating whether an AutoML result is production-ready.