KubeRay v1.5 introduces significant enhancements to the Ray on Kubernetes ecosystem, including increased security, performance, and scalability. This release incorporates over 180 commits from around 50 contributors, offering features such as full support for Ray authentication token mode to secure cluster access, incremental upgrades in RayService to reduce resource overhead, and new submission modes in RayJob to streamline job management. Advanced deletion policies allow more flexible resource cleanup while a lightweight job submitter improves startup times. Integration with batch schedulers like Yunikorn and Volcano enables efficient gang scheduling, while the new Ray Label Selector API enhances autoscaling decisions. Additionally, KubeRay v1.5 integrates with NVIDIA's Kai-Scheduler for gang scheduling and workload prioritization, and it enhances the API Server and Dashboard for improved user experience. The update also includes a tuning guide for large-scale deployments, enabling more effective management of extensive Kubernetes clusters.