What is a grid search method?

Grid search is a hyperparameter optimization technique used in machine learning to systematically explore a specified subset of hyperparameter space. It involves defining a grid of hyperparameter values and evaluating the model's performance for every combination of these values through cross-validation. This brute-force approach helps identify the best hyperparameter settings that yield the highest model accuracy or performance metrics. Although effective, grid search can be computationally expensive, especially with a large number of hyperparameters or extensive value ranges.