Sums of squares (SOS) relaxations provide efficiently computable lower bounds for minimization of multivariate polynomials. Practical experience has shown that these bounds usually outperform most other available techniques, but a fully satisfactory theoretical justification is still lacking. In this talk, we discuss several results (new and old) about the approximation quality of these SOS bounds, focusing on the case of polynomial optimization on the sphere.