Traffic signal control is of critical importance for the effective use of transportation infrastructures. Unfortunately, the rapid increase of different types of vehicles makes traffic signal control more and more challenging. Reinforcement Learning (RL) based algorithms have demonstrated their potential in dealing with traffic signal control. However, most existing solutions would require a large amount of training data, which is unacceptable for many real-world scenarios. This paper proposes a novel model-based meta-reinforcement learning framework (ModelLight) for traffic signal control. Within ModelLight, an ensemble of models for road intersections and the optimization-based meta-learning method are used to improve the data efficiency of an RL-based traffic light control method. Experiments on real-world datasets demonstrate that the proposed ModelLight can outperform state-of-the-art traffic light control algorithms while substantially reducing the required interactions with the real-world environment.