Grid Search is a systematic method used in machine learning to find the best combination of hyperparameters for a model. It involves defining a grid of possible values for each hyperparameter and then evaluating the model's performance for every combination. This helps in identifying the optimal settings that yield the best results. The process typically includes splitting the data into training and validation sets. For each combination of hyperparameters, the model is trained on the training set and tested on the validation set. The combination that results in the highest performance metric, such as accuracy or F1 score, is selected as the best configuration.