Dropout is a regularization technique for neural networks where randomly selected neurons are temporarily ignored during each training step, forcing the network to learn redundant representations rather than relying on any single neuron. It is like a team where random members sit out each practice session, making the remaining players more versatile. Dropout significantly reduces overfitting and is one of the most widely used tricks in deep learning, applied in vision, language, and recommendation models.