Answer
During each training step, a percentage of neurons are randomly ignored, which forces the network to learn more general and robust patterns instead of memorizing the training data.
Real-world example A deep learning model trained on a small image dataset uses dropout to avoid memorizing the exact training photos and instead learn general visual patterns.