More data devoted to validation affects model assessments in what way?

When more data is allocated to validation, it significantly enhances the stability of model assessments. Stability here refers to the model's ability to maintain consistent performance across different subsets of data. By using a larger validation set, the evaluations obtained are less influenced by any peculiarities or noise in smaller data samples, leading to a more reliable estimate of how the model will perform on unseen data.

With a robust and larger validation dataset, the results become less susceptible to random variations and outliers that may skew performance metrics when assessed with smaller datasets. Consequently, this strengthens confidence that the model's performance reflects its true predictive capability rather than being an artifact of an insufficiently sized validation sample.

While the other concepts such as complexity, variability, and inaccuracy may play a role in different contexts of model evaluation or development, they do not directly relate to how increased validation data specifically improves model assessment's stability. Ensuring a stable performance evaluation is crucial in model selection and tuning processes, making the emphasis on increased validation data a fundamentally important aspect of the modeling workflow.