Momentum is a widely-used optimization technique in machine learning, particularly in training deep neural networks. It enhances the gradient descent algorithm by introducing a velocity component that helps accelerate convergence in directions of low curvature while stabilizing updates in high curvature areas. By accumulating past gradients, momentum smooths out oscillations and allows the optimization process to navigate through narrow valleys and local minima more effectively. This results in faster training times and improved performance. Understanding momentum and its tuning is essential for practitioners aiming to optimize their models efficiently.