Bayesian Optimization involves constructing a surrogate model, typically a Gaussian Process, to approximate the objective function. The model is updated as new data points are evaluated, and an acquisition function is used to determine the next point to sample. This iterative process continues until the optimal solution is found or a stopping criterion is met.