The Cluster Autoscaler (CA) in Kubernetes addresses infrastructure bottlenecks by automatically adjusting the number of nodes in a cluster based on resource demand, ensuring optimal capacity management. It adds nodes when pods can't be scheduled and removes idle nodes to save resources, complementing other autoscalers like the Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA), which focus on pod behavior. CA's effectiveness hinges on its integration with cloud providers, working with Kubernetes-managed platforms like GKE, EKS, and AKS, as well as self-managed clusters through auto-scaling groups. Although CA offers significant benefits in managing infrastructure cost and capacity, it has limitations, including slow scale-up response times and potential service disruptions during scale-down. It lacks awareness of workload efficiency, cost considerations, and is unable to optimize resource distribution across nodes. Best practices for CA involve configuring node pools, setting conservative scale-down parameters, and using it in conjunction with HPA and VPA to enhance decision-making. Additionally, advanced strategies like using spot node groups, GPU-aware scaling, and label-aware scaling can further optimize resource use, with tools like DevZero addressing CA's operational constraints by proactively managing resource requests and tracking cost implications.