t-distributed Stochastic Neighbor Embedding (t-SNE) is a machine learning technique used for visualizing high-dimensional data in a lower-dimensional space, typically two or three dimensions. It works by converting similarities between data points into probabilities, allowing similar points to be placed closer together while dissimilar points are pushed further apart. This helps in revealing patterns and structures in complex datasets. The method uses a probability distribution based on the Student's t-distribution to maintain the local structure of the data while also capturing global relationships. t-SNE is particularly useful in fields like bioinformatics and computer vision, where understanding intricate data relationships is crucial.