Existing Domain Adaptation Object Detection (DAOD) works usually report their performance by selecting the best model on the validation set or even the test set of the target domain. This is highly impractical in real-world applications, where the target domain annotated data is often extremely scarce. In this paper, we propose a novel unsupervised model selection approach for domain adaptive object detection, which is able to select almost the optimal model for the target domain without using any target labels. Our approach is based on the flat minima principle, i.e., models that locate the flat minima region in the parameter space usually exhibit excellent generalization ability. However, traditional ways require labeled data to evaluate how well a model is located at the flat minima region, which is unrealistic for the DAOD task. Therefore, we design a Detection Adaptation Score (DAS) approach to approximately measure the flat minima without using target labels. We show via the generalization bound that the flatness can be deemed as model variance, while the minima depend on the domain distribution distance for the DAOD task. Accordingly, we propose a flatness index score (FIS) to assess the flatness by measuring the classification and localization fluctuation before and after perturbations of model parameters, and a prototypical distance ratio (PDR) score to seek the minima by measuring the transferability and discriminability of the models. In this way, the proposed DAS approach can effectively represent the degree of flat minima and evaluate the model generalization ability on the target domain. We have conducted extensive experiments on various DAOD benchmarks and approaches, the experimental results show that the proposed DAS well correlates with the performance of DAOD models and can be used as an effective tool for model selection after training. The code will be available soon.