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 term that helps accelerate convergence in directions of low curvature while dampening oscillations in high curvature areas. This results in faster and more stable training. The momentum parameter, often set to a default value of 0.9, can significantly influence the optimization process. By effectively navigating the loss landscape, momentum helps prevent issues like overshooting minima and can improve overall model performance. Understanding and tuning momentum is crucial for successful machine learning applications.