Random search is a simple optimization technique used to find the best solution among a set of possibilities. Instead of systematically exploring all options, it randomly samples a subset of potential solutions. This method is particularly useful when the search space is large and complex, making exhaustive search impractical. In machine learning, random search can be applied to hyperparameter tuning, where it helps identify the best parameters for models like decision trees or neural networks. By randomly selecting combinations of hyperparameters, it can often find effective configurations more quickly than grid search, which tests every possible combination systematically.