Abstract We study three fundamental statistical learning problems: distribution estimation, property estimation, and property testing. We establish the profile maximum likelihood (PML) estimator as the first unified sample-optimal approach to a wide range of learning tasks. In particular, for every alphabet size k and desired accuracy ε: Distribution estimation Under distance, PML yields optimal sample complexity for sorted distribution estimation, and a PML-based estimator empirically outperforms the Good-Turing estimator on the actual distribution; Additive property estimation For a broad class of additive properties, the PML plug-in estimator uses just four times the sample size required by the best estimator to achieve roughly twice its error, with exponentially higher confidence; α-Rényi entropy estimation For an integer α > 1, the PML plug-in estimator has optimal sample complexity; for non-integer α > 3/4, the PML plug-in estimator has sample complexity lower than the state of the art; Identity testing In testing whether an unknown distribution is equal to or at least ε far from a given distribution in distance, a PML-based tester achieves the optimal sample complexity up to logarithmic factors of k. With minor modifications, most of these results also hold for a near-linear-time computable variant of PML.