= Solution
One suitable criterion among the three unpenalized <normal linear models> is the <Akaike information criterion>. With $d$ estimated mean coefficients and the variance also estimated, minimizing AIC is equivalent to minimizing
$$
\boxed{\operatorname{AIC}=n\log(RSS/n)+2(d+1)+\text{a common constant}.}
$$
The values of $d$ are $2,2,3$ for model1, model2 and model3. This compares fit and complexity rather than choosing solely by individual coefficient significance. The <Bayesian information criterion> or a common-fold predictive comparison would also be reasonable, depending on the selection objective.
For the <ridge regression> tuning parameter, use <K-fold cross-validation> with squared prediction loss:
$$
\boxed{CV(\lambda)=\frac1n\sum_{k=1}^K\sum_{i\in I_k}\{y_i-\widehat f_{-I_k,\lambda}(x_i)\}^2.}
$$
Use the same folds for every candidate, estimate any centering or scaling within each training fold, choose a minimizing $\lambda$, and refit on all observations. This estimates <mean squared prediction error> while avoiding <data leakage>. The printed summaries do not supply all residual sums of squares or validation losses, so they do not determine which model or penalty wins.
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