t-Distributed Stochastic Neighbor Embedding, or t-SNE, is a technique used to visualize 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 makes it easier to see patterns and clusters in complex datasets. The method is particularly useful in fields like machine learning and data science, where understanding relationships in large datasets is crucial. By using t-SNE, researchers can gain insights into the structure of their data, helping to identify trends and anomalies that might not be visible in higher dimensions.