Model evidence, also known as the marginal likelihood, is a crucial component in Bayesian model comparison as it quantifies how well a model explains the observed data, balancing fit and complexity. It is used to compute the Bayes factor, which helps in selecting the most probable model among a set of candidates by considering both the data likelihood and prior information.