Example Concept: Bayesian Optimization is a probabilistic model-based optimization technique that builds a surrogate model to approximate the objective function. It iteratively selects hyperparameters by balancing exploration and exploitation, often using Gaussian Processes. This approach is more efficient than exhaustive methods like Grid Search, especially for complex models with large hyperparameter spaces.