Zero-inflated models are statistical models used to handle excess zeros in count data, which traditional Poisson or negative binomial models fail to accommodate. They work by combining a count model with a binary model to separately account for the zero-generating process, providing a more accurate fit for datasets with an overabundance of zero observations.