Mode Collapse is a challenge in GAN training where the Generator produces a limited variety of outputs, failing to capture the full diversity of the data distribution. This problem occurs when the Generator finds a few modes that consistently fool the Discriminator, leading to repetitive or less varied generated samples. Addressing mode collapse involves using techniques such as improved training strategies or model architectures to encourage the Generator to explore and produce a broader range of outputs.