Eventual consistency is a consistency model used in distributed databases where, after updates, all replicas of the data will eventually converge to the same value, but not necessarily immediately. This model improves system availability and performance, especially in distributed and large-scale systems. It supports high availability and partition tolerance, as explained by the CAP theorem, which states that a distributed system can only guarantee two of three properties: Consistency, Availability, and Partition Tolerance.

Key trade-offs:

1. Immediate Consistency vs. Availability: Traditional databases ensure immediate consistency (all users see the same data instantly), while eventual consistency allows for higher availability and fault tolerance. This enables parts of the system to operate and process requests even if others are down.
2. Data Accuracy: Eventual consistency can lead to temporary periods where different parts of the system show different data, causing discrepancies and outdated information until all replicas are synchronized.
3. Complexity: Implementing and managing eventual consistency requires mechanisms like conflict resolution and data reconciliation to handle inconsistencies during synchronization.

In essence, eventual consistency trades some immediate data accuracy for greater system resilience and availability.