t-SNE, or t-distributed Stochastic Neighbor Embedding, is a machine learning technique used for visualizing high-dimensional data in a lower-dimensional space, typically two or three dimensions. It helps to reveal patterns and relationships in complex datasets by preserving local structures, making it easier to identify clusters and similarities among data points. The algorithm works by converting the similarities between data points into probabilities, then minimizing the divergence between these probabilities in the high-dimensional and low-dimensional spaces. This results in a visual representation that highlights the inherent structure of the data, making t-SNE a popular tool in fields like data science and bioinformatics.