Tuning hyperparameters is essential for optimizing model performance. Key factors include learning rate, batch size, momentum, and weight decay. Understanding their interactions with the optimizer can prevent issues like divergence and overfitting. Techniques such as cross-validation and Bayesian optimization enhance hyperparameter selection for better generalization.