Pooling layers are a crucial component in convolutional neural networks, primarily used to reduce the spatial dimensions of feature maps, thereby decreasing computational load and controlling overfitting. They achieve this by summarizing the presence of features in patches of the feature map, often using operations like max pooling or average pooling.