Random Search is a hyperparameter optimization technique that involves randomly sampling from the hyperparameter space and evaluating performance, offering a simple yet effective approach for exploring large search spaces. It can often find good solutions faster than grid search by not being constrained to a fixed search pattern, making it particularly useful when dealing with high-dimensional spaces or when computational resources are limited.