Making an informed choice between competing physical models becomes increasingly important in the era of precision cosmology. Central to model selection is a trade-off between performing a good fit and low model complexity: A model of higher complexity should only be favoured over a simpler model if it provides significantly better fits. In Bayesian terms, this can be achieved by considering the evidence ratio, enabling choices between two competing models. We generalise this concept by constructing Markovian random walks in model space governed by the logarithmic evidence ratio. This is in analogy to the logarithmic likelihood ratio in parameter estimation problems. We apply our methodology to selecting a polynomial for the dark energy equation of state function based on data for the supernova distance-redshift relation.