We prove upper and lower bounds on the VC dimension of sparse univariate polynomials over reals, and apply these results to prove uniform learnability of sparse polynomials and rational functions. As another application we solve an open problem of Vapnik ([Vapnik 82]) on uniform approximation of the general regression functions, a central problem of computational statistics (cf. [Vapnik 82]), p. 256).