How do you tune hyperparameters efficiently (grid search vs random vs Bayesian)?

Grid search is simple but expensive. Random search covers space more efficiently. Bayesian optimization uses past trials to pick better candidates. Use: - Random search for quick wins - Bayesian for expensive models - Early stopping where possible Always avoid tuning on the test set and track experiments for reproducibility.