Mlxtend offers supplementary tools for machine learning workflows, including stacking and voting classifiers, feature extraction and selection techniques, and visualization helpers like decision region plots. It also includes frequent pattern mining implementations such as the Apriori algorithm for association rule mining. The library extends common data science libraries with utilities meant to complement rather than replace core ML frameworks.