Latent space is a representation of compressed data where complex features are encoded in a lower-dimensional space, often used in machine learning to simplify data and reveal underlying patterns. It enables efficient data manipulation and transformation, facilitating tasks such as dimensionality reduction, data generation, and feature extraction.