Hyperparameter tuning is crucial for optimizing model performance. Key techniques include grid search, random search, and cross-validation. Libraries like TensorFlow's Keras Tuner, Optuna, and Hyperopt facilitate automation and advanced optimization methods. Important hyperparameters include learning rate, batch size, and model architecture.