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 If the goal is to achieve robust performance across contexts or datasets, whenever possible we recommend directly evaluating generalization; otherwise, we recommend choosing the model that performs the best on unseen data via cross-validation.
 
 
-<!-- Without directly evaluating model generalization, it is tempting to assume that simpler models will generalize better than more complex models.
-Studies in the statistics and machine learning literature suggest this rule of thumb [@doi:10.1214/088342306000000060; @doi:10/bhfhgd; @doi:10.4137/CIN.S408; @doi:10.1371/journal.pcbi.1004961], and model selection approaches sometimes incorporate criteria to encourage simpler models that do not fit the data as closely.
-These ideas have taken root in genomics, although they are less commonly stated formally or studied systematically [@doi:10.1007/s00405-021-06717-5; @doi:10.1089/dna.2020.6193; @doi:10.1186/s12859-021-04503-y].
-However, we do not observe strong evidence that simpler models inherently generalize more effectively than more complex ones.
-There may be other reasons to train small models or to look for the best model of a certain size/sparsity, such as biomarker interpretability or assay cost.
-Our results underscore the importance of defining clear goals for each analysis.
-If the goal is to achieve generalization across contexts or datasets, whenever possible we recommend directly evaluating generalization.
-When it is not feasible, we recommend choosing the model that performs the best on unseen data via cross-validation or a holdout dataset. -->
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 ## Highlights
 
 * Systematic evaluation of generalization of cancer transcriptomics predictive models