To tune the hyperparameters of a machine learning model, you can try different combinations of hyperparameters and evaluate the model's performance on a validation set. One common approach is to use a grid search or a randomized search over a range of hyperparameter values to find the best combination. Additionally, more advanced techniques such as Bayesian and gradient-based optimization can be used to tune hyperparameters more efficiently.