Hyperparameter tuning is the systematic process of finding optimal configuration values for machine learning models to maximize performance. Unlike model parameters learned during training (such as weights in neural networks), hyperparameters are set before training begins and control the learning process itself—learning rate, batch size, number of layers, regularization strength, and optimizer choice. Effective tuning can mean the difference between a model that barely outperforms random guessing and one that achieves state-of-the-art results. The key challenge lies in navigating vast, high-dimensional search spaces efficiently: exhaustive search becomes computationally infeasible as the number of hyperparameters grows, making intelligent search strategies essential for practical machine learning workflows.