Spectral Normalization is a technique used to stabilize the training of GANs by normalizing the weights of the Discriminator. This method controls the spectral norm of weight matrices, preventing issues such as exploding or vanishing gradients that can destabilize training. Spectral Normalization helps ensure more stable and reliable training of generative models, leading to improved quality of generated data and more consistent training dynamics.