Random Search is a simple optimization technique used to find the best solution among a set of possible options. Instead of systematically exploring all possibilities, it randomly samples a subset of solutions. This approach can be particularly useful when the search space is large and complex, allowing for quicker identification of good solutions without exhaustive computation. In machine learning, Random Search is often employed for hyperparameter tuning. By randomly selecting combinations of hyperparameters, it can efficiently discover configurations that improve model performance. This method is generally more effective than grid search, especially when only a few hyperparameters significantly impact the outcome.