Latent Space is a high-dimensional representation where data is compressed into a more abstract form. In generative models, latent space captures the essential characteristics of the data, allowing for the generation of new samples by navigating and sampling from this space. The structure of latent space helps models produce varied and realistic outputs based on the learned distribution.