Pooling is a technique used in convolutional neural networks to reduce the spatial dimensions of feature maps, which helps decrease computational load and control overfitting. It works by summarizing the presence of features in a region, typically using operations like maximum or average pooling, and is crucial for efficient image recognition tasks.