Hyperparameter tuning in AutoML is typically automated using methods such as grid search, random search, and Bayesian optimization. Grid search exhaustively searches through a manually specified subset of hyperparameters, random search samples hyperparameters randomly, and Bayesian optimization uses probability to find the best hyperparameters. These methods enhance the performance and efficiency of the model training process by systematically exploring the hyperparameter space and identifying the optimal configuration for the model.