Bayesian Optimization is a statistical technique used to find the maximum or minimum of a function that is expensive to evaluate. It builds a probabilistic model of the function and uses this model to make informed decisions about where to sample next. This approach is particularly useful in scenarios where evaluations are costly, such as tuning hyperparameters in machine learning models. The process involves two main components: a surrogate model, which approximates the function, and an acquisition function, which determines the next point to sample based on the surrogate model. By iteratively updating the model with new data, Bayesian Optimization efficiently explores the search space to identify optimal solutions.