A financial services company is building a machine learning model to predict loan defaults, but the data science team is struggling to find the right balance between model complexity and accuracy. They are aware of the bias-variance trade-off, as understanding this trade-off is critical for optimizing the model’s performance and ensuring it generalizes well. What is the bias versus variance trade-off in machine learning?