Tuning hyperparameters is crucial for optimizing model performance in machine learning. Key settings include learning rate, batch size, epochs, and dropout rate. Techniques like learning rate schedulers, early stopping, and automated search methods such as Optuna can enhance training outcomes. Effective hyperparameter tuning leads to improved model accuracy.