Random projections are a dimensionality reduction technique that preserves the distances between points with high probability, making them useful for efficiently handling high-dimensional data. They leverage the Johnson-Lindenstrauss lemma to project data into a lower-dimensional space while maintaining the geometric structure of the original dataset.