Graphon, short for graph function, provides a generative model for networks. An accurate estimation of graphon plays a key role in many applications, such as link prediction. In recent decades, various methods for graphon estimation have been proposed. The success of most graphon estimation methods depends on a proper specification of hyperparameters. Some network cross-validation methods have been proposed, but they suffer from restrictive model assumptions, expensive computational costs, and a lack of theoretical guarantees. To address these issues, we propose a masked mirror validation (MMV) method. Asymptotic properties of the MMV are established. The effectiveness of the proposed method in terms of both computation and accuracy is demonstrated by extensive simulation experiments. We further apply MMV for drug repurposing in a real data application.