The field of ecological niche modeling or species distribution modeling has seen enormous activity and attention in recent years, in light of exciting biological inferences that can be drawn from correlational models of species’ environmental requirements (i.e., ecological niches) and inferences of potential geographic distributions. Among the many methods used in the field, one or two are in practice assumed to be ‘best’ and are used commonly, often without explicit testing. We explore herein implications of the “No Free Lunch” theorem, which suggests that no single optimization approach will prove to be best under all circumstances: we developed diverse virtual species with known niche and dispersal properties to test a suite of niche modeling algorithms designed to estimate potential areas of distribution. The result was that (1) indeed, no single ‘best’ algorithm was found, and (2) different algorithms perform in very different manners depending on the particularities of the virtual species. The conclusion is that niche or distribution modeling studies should begin by testing a suite of algorithms for predictive ability under the particular circumstances of the study, and choose an algorithm for a particular challenge based on the results of those tests. Studies that do not take this step may use algorithms that are not optimal for that particular challenge.