Cloud-native is an approach to building and running scalable applications in modern cloud infrastructures, with the Kubernetes container orchestration platform being often considered as a fundamental cloud-native building block. In this paper, we evaluate alternative execution models for scientific workflows in Kubernetes. We compare the simplest job-based model, its variant with task clustering, and finally we propose a cloud-native model based on microservices comprising auto-scalable worker-pools. We implement the proposed models in the HyperFlow workflow management system, and evaluate them using a large Montage workflow on a Kubernetes cluster. The results indicate that the proposed cloud-native worker-pools execution model achieves best performance in terms of average cluster utilization, resulting in a nearly 20\% improvement of the workflow makespan compared to the best-performing job-based model. However, better performance comes at the cost of significantly higher complexity of the implementation and maintenance. We believe that our experiments provide a valuable insight into the performance, advantages and disadvantages of alternative cloud-native execution models for scientific workflows.