GlobalAveragePooling2D in Keras reduces convolutional layer outputs from 3D to 1D tensors, improving model efficiency and spatial invariance. Typical CNN models use Conv2D layers followed by GlobalAveragePooling2D before Dense layers for multi-class classification with optimized parameter reduction.