Spectral partitioning is a technique used in graph theory to divide a graph into clusters by leveraging the eigenvalues and eigenvectors of its Laplacian matrix. It is particularly effective for minimizing the number of edges between different clusters, making it useful for applications in network analysis, image segmentation, and parallel computing.