L1 norm minimization is a mathematical optimization technique used primarily for promoting sparsity in solutions, particularly in high-dimensional data contexts. It is widely employed in fields like compressed sensing and machine learning due to its ability to produce simpler, more interpretable models by effectively selecting relevant features.