Bayesian Optimization is an efficient method for optimizing expensive-to-evaluate functions by building a probabilistic model and selecting the most promising points to evaluate based on a trade-off between exploration and exploitation. It is particularly useful in hyperparameter tuning for machine learning models and other complex optimization problems where traditional methods are computationally prohibitive.