t-SNE (t-Distributed Stochastic Neighbor Embedding) is a dimensionality reduction technique specifically designed for visualizing high-dimensional data in 2D or 3D. It maps similar data points close together and dissimilar ones far apart, revealing cluster structure that would be invisible in raw high-dimensional space. t-SNE is widely used to visualize embeddings, explore dataset structure, and present model behavior. However, it is slow on large datasets and primarily for visualization — not for preprocessing before model training.