While many process mining algorithms have been proposed recently, there does not exist a widely-accepted benchmark to evaluate and compare these process mining algorithms. As a result, it can be difficult to choose a suitable process mining algorithm for a given enterprise or application domain. Some recent benchmark systems have been developed and proposed to address this issue. However, evaluating available process mining algorithms against a large set of business models (e.g., in a large enterprise) can be computationally expensive, tedious and time-consuming. This paper investigates a scalable solution that can evaluate, compare and rank these process mining algorithms efficiently, and hence proposes a novel framework that can efficiently select the process mining algorithms that are most suitable for a given model set. In particular, using our framework, only a portion of process models need empirical evaluation and others can be recommended directly via a regression model. As a further optimization, this paper also proposes a metric and technique to select high quality reference models to derive an effective regression model. Experiments using artificial and real datasets show that our approach is practical and outperforms the traditional approach.