Uniform Manifold Approximation and Projection (UMAP) is a dimensionality reduction technique that helps visualize high-dimensional data in a lower-dimensional space. It preserves the local structure of the data while maintaining a global view, making it useful for tasks like clustering and visualization. UMAP works by modeling the data as a manifold, which is a mathematical space that locally resembles Euclidean space. It then optimizes the layout of points in the lower-dimensional space to reflect the relationships found in the original data, making it a powerful tool for data analysis and exploration.