Grid search is a systematic method used in machine learning to find the best combination of hyperparameters for a model. It involves defining a set of values for each hyperparameter and then evaluating the model's performance for every possible combination of these values. This helps in identifying the optimal settings that yield the best results. The process typically involves splitting the data into training and validation sets. After training the model with each combination, the performance is measured using metrics like accuracy or F1 score. The combination that produces the best performance is then selected for the final model.